I gave a couple of presentations at the Pacific Northwest Software Quality Conference in Portland this week: a case study about a rousing success we had with HP in Barcelona, and a presentation about what each of us as individuals can do to improve overall quality in collaborative teams. The conference uses green, yellow and red cards to allow people to rate the talk immediately, with the option of providing some written feedback. While both talks were very well received, one person responded to my 'individuals' talk with the following comment on a red card: "the correlation chart, the last entry about length of employment - you mean active engagement - the substitution of length of employment is why age discrimination mean I expect to lose my job - may you rot in your smugness". Woah! Let me respond...
The data that we are talking about here is results from the diagnostics we have run in many groups over the years, and now comprises over 700 sets of responses to 35 questions on demographics, practices and performance. I had done a regression to see if there was any correlation across the entire data set between practices/performance, and demographics/performance. The results revealed that the strongest positive correlations were the things we think of as 'best-practices', but often don't take the time to do on our projects. I see this as validating precisely what I try to do with software teams.
Down at the other end of the scale, the data shows the strongest negative correlation to performance (about a -0.2 correlation, averaged over 4 different performance categories) comes from something that is labelled 'Employment with Company' on the slide. The underlying data is saying that as the average company tenure across the team for the group taking the diagnostic increases, there is a tendency for these teams to rank themselves lower in performance across 4 different measures. A mouthful, but it is critical to note this is not massaged or interpreted in any way, it is merely a data point. We get similar negative data points for the amount of effort people typically spend on projects, their average industry experience, and the number of professional designations in the group.
These data points generally are the source for very rich discussion and debate.
At the end of a conference presentation, though, there is rarely the opportunity to dive into deeper meaning of the data and a rich discussion about how to interpret what we see. When asked about it, I indicated that I had seen in some shops, there were some people that can tend to find their current job as a comfortable place to stay, which seemed to satisfy the inquirer, but clearly failed to appease the anonymous commenter. There is a world of additional depth to that data point, both from my perspective and experience, and clearly from the experience and concerns of other people in attendance. I have also seen that people with longer tenure can sometimes fail to stay current with new technologies, but have also seen people with long tenure that remain active contributors and lead the charge to stay at the forefront of technological shift. Given that the response rate for this data is abnormally high indicates that some of the responses may have come from the people who would tend to not be asked or would choose not to respond with other approaches, which may also bias the data in a certain direction. Where that bias drives us is a topic for deeper discussion, which I am certainly open to.
The very statistical definition of correlation drives us to recognize this is not black and white data. Indeed the relatively low correlation, while negative, indicates that it is not statistically significant. There are no absolutes in this data, every data point tells us a rich story, if we choose to have our ears on. Given the current economy and the outlook for employment, it is easy to understand this sort of visceral response to the data and my brief, incomplete interpretation.
Remember, though, that this data point and reaction were in the context of a talk about what we as individuals can do to improve collaboration. Just as the data point I presented described some correlation that we can interpret in different ways, the data point that there is danger of being let go can also be interpreted in different ways. I had pressed the point in the presentation that we need to ask ourselves what our contribution is to any situation, and what are the things we can do about that situation. While age discrimination may exist in some places and job inertia may exist in others, either way there are always things we can do.
I'll take that comment about my smugness as a reaction to the data that was presented, combined with my intent to stand in front of the group with self-confidence (which was apparently interpreted as being overdone here). I still don't see myself as smug, and don't think that any data point should drive a specific interpretation or action. Conference presentations are not the best avenue for deep discussion and shared understanding, and I welcome the person who provided that feedback to contact me for a chat.
But I don't plan on rotting in the meantime.
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